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Bayesian Computation using Design of Experiments-based Interpolation Technique in R
Output the normalising constant for the target density.
design = data.frame(x=rnorm(10), y=rnorm(10))
design$f = with(design, exp(-0.5*(x+y)^2))
fit = doit_fit(design)
x_eval = data.frame(x=seq(-3,3,.1), y=NA)
mar_x = doit_marginal(fit, 'x', theta_eval=x_eval)
Error in data.frame(par = colnames(theta)[k], theta = theta_eval, dens_approx = ans) :
arguments imply differing number of rows: 1, 61, 2
In addition: Warning messages:
1: In d_ij * phi_ij :
longer object length is not a multiple of shorter object length
2: In d_ij * phi_ij :
longer object length is not a multiple of shorter object length
use nloptr and analytic expression of the gradient to speed up the optimisation
Add argument near
in doit_propose_new
to specify a starting point for the maximisation of the estimation variance.
Add argument fix
to fix values for one or more parameters and only maximise the variance along the remaining ones.
Adding support for defining a fixed correlation matrix by user
Use the kernel representation to estimate the density of a linear transformation A %*% theta by a matrix A. This would include the marginals and joint marginals (#3), as well as transformations such as theta[1]+theta[2]
.
to illustrate 2d joint marginals
Allow multiple parameter dimensions in doit_marginal
and return approximation of the joint marginal density.
doit_marginal(doit, theta_dims=c('theta1', 'theta2'), theta_eval=theta_eval)
where theta1
and theta2
are column names of theta_eval
. If theta_dims
is unspecified, the joint marginal over all columns of theta_eval
is calculated.
doit_fit
and doit_update
check for duplicate design pointsGGfun
@seealso
tags?doit
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